Gestational diabetes mellitus (GDM) is a pregnancy disorder associated with short‐ and long‐term adverse outcomes in both mothers and infants. The current clinical test of blood glucose levels late in the second trimester is inadequate for early detection of GDM. Here we show the utility of Raman spectroscopy (RS) for rapid and highly sensitive maternal metabolome screening for GDM in the first trimester. Key metabolites, including phospholipids, carbohydrates, and major amino acids, were identified with RS and validated with mass spectrometry, enabling insights into associated metabolic pathway enrichment. Using classical machine learning (ML) approaches, we showed the performance of the RS metabolic model (cross‐validation AUC 0.97) surpassed that achieved with patients' clinical data alone (cross‐validation AUC 0.59) or prior studies with single biomarkers. Further, we analyzed novel proteins and identified fetuin‐A as a promising candidate for early GDM prediction. A correlation analysis showed a moderate to strong correlation between multiple metabolites and proteins, suggesting a combined protein‐metabolic analysis integrated with ML would enable a powerful screening platform for first trimester diagnosis. Our study underscores RS metabolic profiling as a cost‐effective tool that can be integrated into the current clinical workflow for accurate risk stratification of GDM and to improve both maternal and neonatal outcomes.

中文翻译：

---

通过拉曼光谱和蛋白质分析诊断妊娠早期患者血浆妊娠障碍


妊娠糖尿病 （GDM） 是一种与母亲和婴儿的短期和长期不良结局相关的妊娠疾病。目前对妊娠中期晚期血糖水平的临床测试不足以进行 GDM 的早期检测。在这里，我们展示了拉曼光谱 （RS） 在妊娠早期快速、高灵敏度的母体代谢组筛查 GDM 的效用。用 RS 鉴定关键代谢物，包括磷脂、碳水化合物和主要氨基酸，并用质谱法验证，从而能够深入了解相关的代谢途径富集。使用经典的机器学习 （ML） 方法，我们表明 RS 代谢模型 （交叉验证 AUC 0.97） 的性能超过了单独使用患者临床数据 （交叉验证 AUC 0.59） 或使用单一生物标志物的先前研究所实现的性能。此外，我们分析了新蛋白质并确定胎球蛋白 A 是早期 GDM 预测的有希望的候选者。相关性分析显示多种代谢物和蛋白质之间存在中度至强相关性，这表明与 ML 相结合的蛋白质-代谢分析将为孕早期诊断提供强大的筛查平台。我们的研究强调 RS 代谢分析是一种具有成本效益的工具，可以整合到当前的临床工作流程中，以准确对 GDM 进行风险分层并改善孕产妇和新生儿结局。